Self-Supervised Learning Approaches for Credit Data Representation and Risk Stratification

Santhosh Kumar Sagar Nagaraj

doi:10.63530/IJCSITR_2025_06_04_004

Authors

Santhosh Kumar Sagar Nagaraj Staff Software Engineer, Visa Inc, Banking & Finance, 1745 stringer pass, Leander, Texas 78641, USA. Author

DOI:

https://doi.org/10.63530/IJCSITR_2025_06_04_004

Keywords:

Self-supervised learning, credit risk, data representation, contrastive learning, autoencoder, masked feature prediction, credit scoring, risk stratification, deep learning

Abstract

The complexity and volume of financial data are also rising and require more powerful methods and more intelligent approaches to successful credit risk evaluation. The weakness of many traditional supervised learning algorithms is that they are sensitive to the availability of labelled data, which is scarce, skewed, and costly to acquire in the credit setting. The paradigm of self-supervised learning (SSL), which relies on supervision by the data in its most basic form, has become an attractive alternative option, particularly where labelled data is scarce. This article examines progressive, self-guided instructions for illustrating credit information and risk classification. This is done by creating a system that can create meaningful embeddings of the credit profile of customers using pretext tasks like masked feature prediction, contrastive learning, and autoencoding. Thereafter, the downstream credit risk prediction tasks are performed using such embeddings. We do substantial experimentation on real-world data sets of credit, and compare our models to classic supervised approaches. We have shown that self-supervised models can show a similar or even better performance in credit risk stratification, especially in the setting where there is a limited number of labelled data available. Moreover, we debate the interpretability, deterioration, and generalization abilities of SSL-based models in financial use cases. We also give an insight into how different Tasks implemented in SSL and architecture options affect the goodness of representations learned. The paper ends with a debate on how self-supervised learning will transform risk management in financial services by helping them create fairer, precise, and efficient credit rating models.

References

Hand, D. J., & Henley, W. E. (1997). Statistical classification methods in consumer credit scoring: a review. Journal of the royal statistical society: series a (statistics in society), 160(3), 523-541.

Baesens, B., Setiono, R., Mues, C., & Vanthienen, J. (2003). Using neural network rule extraction and decision tables for credit-risk evaluation. Management science, 49(3), 312-329.

Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020, November). A simple framework for contrastive learning of visual representations. In International Conference on machine learning (pp. 1597-1607). PmLR.

Huang, X., Khetan, A., Cvitkovic, M., & Karnin, Z. (2020). Tabtransformer: Tabular data modelling using contextual embeddings. arXiv preprint arXiv:2012.06678.

Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing data dimensionality with neural networks. science, 313(5786), 504-507.

Liu, X., Zhang, F., Hou, Z., Mian, L., Wang, Z., Zhang, J., & Tang, J. (2021). Self-supervised learning: Generative or contrastive. IEEE Transactions on Knowledge and Data Engineering, 35(1), 857-876.

Lessmann, S., Baesens, B., Seow, H. V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. European Journal of Operational Research, 247(1), 124-136.

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019, June). Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers) (pp. 4171-4186).

Yoon, J., Jarrett, D., & Van der Schaar, M. (2019). Time-series generative adversarial networks. Advances in Neural Information Processing Systems, 32.

Geiping, J., Bauermeister, H., Dröge, H., & Moeller, M. (2020). Inverting Gradients: How Easy Is It to Break Privacy in Federated Learning? Advances in Neural Information Processing Systems, 33, 16937-16947.

Yao, Y. (2024). Self-Supervised Credit Scoring with Masked Autoencoders: Addressing Data Gaps and Noise Robustly. Journal of Computer Technology and Software, 3(8).

Yang, S., Chen, H., Huang, J., Yan, Y., Chen, J., & Xiong, A. (2022, October). Split learning based on self-supervised learning. In International Conference on Computer Engineering and Networks (pp. 95-104). Singapore: Springer Nature Singapore.

Rani, V., Nabi, S. T., Kumar, M., Mittal, A., & Kumar, K. (2023). Self-supervised learning: A succinct review. Archives of Computational Methods in Engineering, 30(4), 2761-2775.

Tian, Y., Yu, L., Chen, X., & Ganguli, S. (2020). Understanding self-supervised learning with dual deep networks. arXiv preprint arXiv:2010.00578.

Li, T., Kou, G., & Peng, Y. (2023). A new representation learning approach for credit data analysis. Information Sciences, 627, 115-131.

Bhatore, S., Mohan, L., & Reddy, Y. R. (2020). Machine learning techniques for credit risk evaluation: a systematic literature review. Journal of Banking and Financial Technology, 4(1), 111-138.

Sui, Y., Wu, T., Cresswell, J. C., Wu, G., Stein, G., Huang, X. S., ... & Volkovs, M. (2023). Self-supervised representation learning from random data projectors. arXiv preprint arXiv:2310.07756.

Self-Supervised Learning Explained, Encord, Online. https://encord.com/blog/self-supervised-learning/

Taherdoost, H. (2024). Beyond supervised: the rise of self-supervised learning in autonomous systems. Information, 15(8), 491.

Yao, T., Yi, X., Cheng, D. Z., Yu, F., Chen, T., Menon, A., ... & Ettinger, E. (2021, October). Self-supervised learning for large-scale item recommendations. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (pp. 4321-4330).